Sidharth Gopakumar
Speaker at IEEE InC4 2026 | Product Manager, AI | Molecule Software
Boston, Massachusetts, United States
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Sidharth Gopakumar is a product manager at Molecule Software, where he leads product development for an energy trading platform used by hedge funds and Fortune 100 energy companies, processing nearly $100B in commodity value daily. Before that, he built conversational GenAI and vector search infrastructure at a YCombinator-backed enterprise startup, taking the platform from zero to 10 enterprise customers. He has published IEEE research on predictive AI frameworks and prescriptive analytics in operations.
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Shipping AI to People Who Don't Trust AI: Product Lessons from Energy Trading
Traders are quantitative, data-driven, and deeply skeptical of anything they can't interrogate. At Molecule Software, we built AI capabilities into our energy trading platform, letting traders query their portfolios and get AI-powered analysis and recommendations on demand. The feature worked. The adoption didn't come easy.
This session covers what it takes to ship AI to domain experts who will interrogate every output and have the authority to simply ignore it. What product decisions drive adoption when the model is the easy part? What does trust-building look like in a high-stakes, regulated domain? Practical lessons that transfer to any domain where users are experts and the stakes are real.
Shipping AI to People Who Don't Trust AI: Product Lessons from Energy Trading
Most teams building with LLMs can tell you if their system is up or down. Very few can tell you why it gave a wrong answer last Tuesday, or which retrieval step failed silently. This session walks through the end-to-end architecture of a production RAG system on Azure, drawing from experience taking a GenAI platform from zero to 10 enterprise customers.
We'll cover how Azure AI Search handles vector indexing and hybrid retrieval, how to wire it to Azure OpenAI for grounded responses, and the practical decisions that separate a working prototype from something you'd trust with enterprise data. Topics include chunking strategies, embedding pipelines, retrieval quality tuning, and where systems break under production load.
RAG in the Real World: Building Enterprise GenAI Pipelines on Azure
Retrieval-Augmented Generation is everywhere in demos — but production enterprise deployments look very different. In this session, I'll walk through the end-to-end architecture of a real RAG system built on Azure, drawing from my experience taking a GenAI platform from zero to 10 enterprise customers at a YCombinator-backed startup.
We'll cover: how Azure AI Search handles vector indexing and hybrid retrieval, how to wire it to Azure OpenAI for grounded, context-aware responses, and the practical decisions that separate a working prototype from something you'd trust with enterprise data. Topics include chunking strategies, embedding pipelines, retrieval quality tuning, and where the system breaks under production load.
Attendees will leave with a clear mental model of the Azure services involved (Azure AI Search, Azure OpenAI, Azure Blob + data pipelines), a realistic picture of what goes wrong and how to debug it, and a starting architecture they can adapt to their own use case. No prior RAG experience needed — intermediate Azure familiarity is sufficient.
From Prototype to Production: A PM's Guide to Getting LLM Features Shipped
Most organizations don't struggle to build an AI prototype. They struggle to get it into production. The blockers aren't technical — they're organizational: legal wants auditability, compliance wants guardrails, expert users will catch every mistake, and leadership wants to know when the model is wrong.
This session is a product manager's guide to the decisions that determine whether an LLM feature ships or stalls. Drawing on experience building GenAI platforms at a YCombinator-backed startup and leading AI product development at an enterprise SaaS used by hedge funds and Fortune 100 companies, the talk covers: how to scope what the model can and can't own in a high-stakes workflow; how to design evaluation frameworks that satisfy both product and compliance teams; how to communicate model limitations to expert users who will test your system aggressively; and how to build the organizational trust that gets AI features past legal, risk, and the CISO.
Practical and non-commercial, designed for product leaders, AI practitioners, and anyone trying to close the gap between a working demo and a trusted production system.
AI Governance for High-Stakes Platforms: Beyond Responsible AI Checklists
Most AI governance frameworks are written for products where a bad output is embarrassing. In financial services and commodity trading, a bad output can trigger regulatory exposure, erode client trust, or generate incorrect positions.
This talk moves past the checklist approach to examine what AI governance actually looks like when deployed in high-stakes production environments. Drawing on experience deploying AI in an energy trading platform processing nearly $100B in commodity value daily, the session covers: auditability requirements for AI decisions that affect financial positions; model drift detection strategies that catch degradation before users do; rollback protocols that don't require taking down the entire system; and how to design human-in-the-loop checkpoints that preserve oversight without killing the user experience.
The talk is practitioner-led and non-commercial, designed for risk leaders, product security teams, and technology executives who are past the policy stage and trying to operationalize governance in production AI systems.
Key takeaways: a practical architecture for AI auditability in high-value transaction environments; how to detect and respond to model drift without disrupting operations; design patterns for human oversight that scale; and where most enterprise AI governance frameworks break down in practice.
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